sample_ctree

Sample ctree variables from a given conditional inference tree

internal

Complex machine learning models are often hard to interpret. However, in
many situations it is crucial to understand and explain why a model made a specific
prediction. Shapley values is the only method for such prediction explanation framework
with a solid theoretical foundation. Previously known methods for estimating the Shapley
values do, however, assume feature independence. This package implements methods which accounts for any feature
dependence, and thereby produces more accurate estimates of the true Shapley values.
An accompanying 'Python' wrapper ('shaprpy') is available through the GitHub repository.

Martin Jullum

shapr

Prediction Explanation with Dependence-Aware Shapley Values

Lars Henry Berge Olsen

Annabelle Redelmeier

Jon Lachmann

Nikolai Sellereite

Anders Løland

Jens Christian Wahl

Camilla Lingjærde

Norsk Regnesentral 

sample_ctree function

<dl><dt>tree</dt>
<dd>List. Contains tree which is an object of type ctree built from the party package.
Also contains given_ind, the features to condition upon.</dd>
<dt>n_MC_samples</dt>
<dd>Scalar integer.
Corresponds to the number of samples from the leaf node.
See an exception when sample = FALSE in <code>setup_approach.ctree()</code>.</dd>
<dt>x_explain</dt>
<dd>Data.table with the features of the observation whose
predictions ought to be explained (test data).</dd>
<dt>x_train</dt>
<dd>Data.table with training data.</dd>
<dt>n_features</dt>
<dd>Positive integer.
The number of features.</dd></dl>

Arguments

Author

Sample ctree variables from a given conditional inference tree — sample_ctree

<dl>

<dt>tree</dt>
<dd>List. Contains tree which is an object of type ctree built from the party package.
Also contains given_ind, the features to condition upon.</dd>


<dt>n_MC_samples</dt>
<dd>Scalar integer.
Corresponds to the number of samples from the leaf node.
See an exception when sample = FALSE in <code>setup_approach.ctree()</code>.</dd>


<dt>x_explain</dt>
<dd>Data.table with the features of the observation whose
predictions ought to be explained (test data).</dd>


<dt>x_train</dt>
<dd>Data.table with training data.</dd>


<dt>n_features</dt>
<dd>Positive integer.
The number of features.</dd>

</dl>

Sample ctree variables from a given conditional inference tree

sample_ctree: Sample ctree variables from a given conditional inference tree

Description

Usage

Value

Arguments

Author

Details